3D shape descriptor for object recognition based on Kinect-like depth image

3D shape descriptor has been used widely in the field of 3D object retrieval. However, the performance of object retrieval greatly depends on the shape descriptor used. The aims of this study is to review and compare the common 3D shape descriptors proposed in 3D object retrieval literature for object recognition and classification based on Kinect-like depth image obtained from RGB-D object dataset. In this paper, we introduce (1) inter-class; and (2) intra-class evaluation in order to study the feasibility of such descriptors in object recognition. Based on these evaluations, local spin image outperforms the rest in discriminating different classes when several depth images from an instance per class are used in inter-class evaluation. This might be due to the slightly consistent local shape property of such images and due to the proposed local similarity measurement that manages to extract the local based descriptor. However, shape distribution performs excellent for intra-class evaluation (that involves several instances per class) may be due to the global shape from different instances per class is slightly unchanged. These results indicate a remarkable feasibility analysis of the 3D shape descriptor in object recognition that can be potentially used for Kinect-like sensor.

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